Merge branch '3.4' into merge-3.4

opencv · Dec 22, 2018 · 1dee705 · 1dee705
2 parents 578ea4b + c0e11bb
commit 1dee705
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diff --git a/cmake/OpenCVDetectApacheAnt.cmake b/cmake/OpenCVDetectApacheAnt.cmake
@@ -1,3 +1,6 @@
+set(OPENCV_JAVA_SOURCE_VERSION "" CACHE STRING "Java source version (javac Ant target)")
+set(OPENCV_JAVA_TARGET_VERSION "" CACHE STRING "Java target version (javac Ant target)")
+
 file(TO_CMAKE_PATH "$ENV{ANT_DIR}" ANT_DIR_ENV_PATH)
 file(TO_CMAKE_PATH "$ENV{ProgramFiles}" ProgramFiles_ENV_PATH)
 

diff --git a/cmake/OpenCVDetectInferenceEngine.cmake b/cmake/OpenCVDetectInferenceEngine.cmake
@@ -78,9 +78,9 @@ endif()
 
 if(INF_ENGINE_TARGET)
   if(NOT INF_ENGINE_RELEASE)
-    message(WARNING "InferenceEngine version have not been set, 2018R4 will be used by default. Set INF_ENGINE_RELEASE variable if you experience build errors.")
+    message(WARNING "InferenceEngine version have not been set, 2018R5 will be used by default. Set INF_ENGINE_RELEASE variable if you experience build errors.")
   endif()
-  set(INF_ENGINE_RELEASE "2018040000" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2018R2.0.2 -> 2018020002)")
+  set(INF_ENGINE_RELEASE "2018050000" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2018R2.0.2 -> 2018020002)")
   set_target_properties(${INF_ENGINE_TARGET} PROPERTIES
     INTERFACE_COMPILE_DEFINITIONS "HAVE_INF_ENGINE=1;INF_ENGINE_RELEASE=${INF_ENGINE_RELEASE}"
   )

diff --git a/doc/tutorials/dnn/dnn_android/dnn_android.markdown b/doc/tutorials/dnn/dnn_android/dnn_android.markdown
@@ -12,7 +12,7 @@ Tutorial was written for the following versions of corresponding software:
 
 - Download and install Android Studio from https://developer.android.com/studio.
 
-- Get the latest pre-built OpenCV for Android release from https://github.com/opencv/opencv/releases and unpack it (for example, `opencv-3.4.4-android-sdk.zip`).
+- Get the latest pre-built OpenCV for Android release from https://github.com/opencv/opencv/releases and unpack it (for example, `opencv-3.4.5-android-sdk.zip`).
 
 - Download MobileNet object detection model from https://github.com/chuanqi305/MobileNet-SSD. We need a configuration file `MobileNetSSD_deploy.prototxt` and weights `MobileNetSSD_deploy.caffemodel`.
 

diff --git a/modules/core/include/opencv2/core/hal/intrin_avx.hpp b/modules/core/include/opencv2/core/hal/intrin_avx.hpp
@@ -1278,6 +1278,16 @@ OPENCV_HAL_IMPL_AVX_CHECK_FLT(v_float64x4, 15)
 OPENCV_HAL_IMPL_AVX_MULADD(v_float32x8, ps)
 OPENCV_HAL_IMPL_AVX_MULADD(v_float64x4, pd)
 
+inline v_int32x8 v_fma(const v_int32x8& a, const v_int32x8& b, const v_int32x8& c)
+{
+    return a * b + c;
+}
+
+inline v_int32x8 v_muladd(const v_int32x8& a, const v_int32x8& b, const v_int32x8& c)
+{
+    return v_fma(a, b, c);
+}
+
 inline v_float32x8 v_invsqrt(const v_float32x8& x)
 {
     v_float32x8 half = x * v256_setall_f32(0.5);

diff --git a/modules/dnn/include/opencv2/dnn/dnn.hpp b/modules/dnn/include/opencv2/dnn/dnn.hpp
@@ -750,6 +750,7 @@ CV__DNN_INLINE_NS_BEGIN
      *  @brief Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.
      *  @param model    path to the file, dumped from Torch by using torch.save() function.
      *  @param isBinary specifies whether the network was serialized in ascii mode or binary.
+     *  @param evaluate specifies testing phase of network. If true, it's similar to evaluate() method in Torch.
      *  @returns Net object.
      *
      *  @note Ascii mode of Torch serializer is more preferable, because binary mode extensively use `long` type of C language,
@@ -771,7 +772,7 @@ CV__DNN_INLINE_NS_BEGIN
      *
      * Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
      */
-     CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true);
+     CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true, bool evaluate = true);
 
      /**
       * @brief Read deep learning network represented in one of the supported formats.

diff --git a/modules/dnn/include/opencv2/dnn/version.hpp b/modules/dnn/include/opencv2/dnn/version.hpp
@@ -6,7 +6,7 @@
 #define OPENCV_DNN_VERSION_HPP
 
 /// Use with major OpenCV version only.
-#define OPENCV_DNN_API_VERSION 20181205
+#define OPENCV_DNN_API_VERSION 20181221
 
 #if !defined CV_DOXYGEN && !defined CV_DNN_DONT_ADD_INLINE_NS
 #define CV__DNN_INLINE_NS __CV_CAT(dnn4_v, OPENCV_DNN_API_VERSION)

diff --git a/modules/dnn/src/layers/mvn_layer.cpp b/modules/dnn/src/layers/mvn_layer.cpp
@@ -116,9 +116,15 @@ class MVNLayerImpl CV_FINAL : public MVNLayer
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
+#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
+            return !zeroDev && eps <= 1e-7f;
+#else
             return !zeroDev && (preferableTarget == DNN_TARGET_CPU || eps <= 1e-7f);
+#endif
         else
+#endif  // HAVE_INF_ENGINE
             return backendId == DNN_BACKEND_OPENCV;
     }
 

diff --git a/modules/dnn/src/onnx/onnx_importer.cpp b/modules/dnn/src/onnx/onnx_importer.cpp
@@ -420,31 +420,30 @@ void ONNXImporter::populateNet(Net dstNet)
         }
         else if (layer_type == "Sub")
         {
-            Mat blob = (-1.0f) * getBlob(node_proto, constBlobs, 1);
-            blob = blob.reshape(1, 1);
+            Mat blob = getBlob(node_proto, constBlobs, 1);
             if (blob.total() == 1) {
                 layerParams.type = "Power";
-                layerParams.set("shift", blob.at<float>(0));
+                layerParams.set("shift", -blob.at<float>(0));
             }
             else {
                 layerParams.type = "Scale";
                 layerParams.set("has_bias", true);
-                layerParams.blobs.push_back(blob);
+                layerParams.blobs.push_back(-1.0f * blob.reshape(1, 1));
             }
         }
         else if (layer_type == "Div")
         {
             Mat blob = getBlob(node_proto, constBlobs, 1);
             CV_Assert_N(blob.type() == CV_32F, blob.total());
-            divide(1.0, blob, blob);
             if (blob.total() == 1)
             {
-                layerParams.set("scale", blob.at<float>(0));
+                layerParams.set("scale", 1.0f / blob.at<float>(0));
                 layerParams.type = "Power";
             }
             else
             {
                 layerParams.type = "Scale";
+                divide(1.0, blob, blob);
                 layerParams.blobs.push_back(blob);
                 layerParams.set("bias_term", false);
             }

diff --git a/modules/dnn/src/op_inf_engine.hpp b/modules/dnn/src/op_inf_engine.hpp
@@ -26,10 +26,11 @@
 #define INF_ENGINE_RELEASE_2018R2 2018020000
 #define INF_ENGINE_RELEASE_2018R3 2018030000
 #define INF_ENGINE_RELEASE_2018R4 2018040000
+#define INF_ENGINE_RELEASE_2018R5 2018050000
 
 #ifndef INF_ENGINE_RELEASE
-#warning("IE version have not been provided via command-line. Using 2018R4 by default")
-#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2018R4
+#warning("IE version have not been provided via command-line. Using 2018R5 by default")
+#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2018R5
 #endif
 
 #define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000))

diff --git a/modules/dnn/src/torch/torch_importer.cpp b/modules/dnn/src/torch/torch_importer.cpp
@@ -129,13 +129,15 @@ struct TorchImporter
     Module *rootModule;
     Module *curModule;
     int moduleCounter;
+    bool testPhase;
 
-    TorchImporter(String filename, bool isBinary)
+    TorchImporter(String filename, bool isBinary, bool evaluate)
     {
         CV_TRACE_FUNCTION();
 
         rootModule = curModule = NULL;
         moduleCounter = 0;
+        testPhase = evaluate;
 
         file = cv::Ptr<THFile>(THDiskFile_new(filename, "r", 0), THFile_free);
         CV_Assert(file && THFile_isOpened(file));
@@ -680,7 +682,8 @@ struct TorchImporter
                     layerParams.blobs.push_back(tensorParams["bias"].second);
                 }
 
-                if (nnName == "InstanceNormalization")
+                bool trainPhase = scalarParams.get<bool>("train", false);
+                if (nnName == "InstanceNormalization" || (trainPhase && !testPhase))
                 {
                     cv::Ptr<Module> mvnModule(new Module(nnName));
                     mvnModule->apiType = "MVN";
@@ -1243,18 +1246,18 @@ struct TorchImporter
 
 Mat readTorchBlob(const String &filename, bool isBinary)
 {
-    TorchImporter importer(filename, isBinary);
+    TorchImporter importer(filename, isBinary, true);
     importer.readObject();
     CV_Assert(importer.tensors.size() == 1);
 
     return importer.tensors.begin()->second;
 }
 
-Net readNetFromTorch(const String &model, bool isBinary)
+Net readNetFromTorch(const String &model, bool isBinary, bool evaluate)
 {
     CV_TRACE_FUNCTION();
 
-    TorchImporter importer(model, isBinary);
+    TorchImporter importer(model, isBinary, evaluate);
     Net net;
     importer.populateNet(net);
     return net;

diff --git a/modules/dnn/test/test_backends.cpp b/modules/dnn/test/test_backends.cpp
@@ -226,9 +226,9 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
 TEST_P(DNNTestNetwork, OpenFace)
 {
 #if defined(INF_ENGINE_RELEASE)
-#if INF_ENGINE_RELEASE < 2018030000
+#if (INF_ENGINE_RELEASE < 2018030000 || INF_ENGINE_RELEASE == 2018050000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
-        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
+        throw SkipTestException("");
 #elif INF_ENGINE_RELEASE < 2018040000
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
         throw SkipTestException("Test is enabled starts from OpenVINO 2018R4");

diff --git a/modules/dnn/test/test_ie_models.cpp b/modules/dnn/test/test_ie_models.cpp
@@ -190,6 +190,14 @@ TEST_P(DNNTestOpenVINO, models)
                                          modelName == "landmarks-regression-retail-0009" ||
                                           modelName == "semantic-segmentation-adas-0001")))
         throw SkipTestException("");
+#elif INF_ENGINE_RELEASE == 2018050000
+    if (modelName == "single-image-super-resolution-0063" ||
+        modelName == "single-image-super-resolution-1011" ||
+        modelName == "single-image-super-resolution-1021" ||
+        (target == DNN_TARGET_OPENCL_FP16 && modelName == "face-reidentification-retail-0095") ||
+        (target == DNN_TARGET_MYRIAD && (modelName == "license-plate-recognition-barrier-0001" ||
+                                         modelName == "semantic-segmentation-adas-0001")))
+        throw SkipTestException("");
 #endif
 #endif
 

diff --git a/modules/dnn/test/test_layers.cpp b/modules/dnn/test/test_layers.cpp
@@ -295,6 +295,10 @@ TEST_P(Test_Caffe_layers, Eltwise)
 {
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
         throw SkipTestException("");
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
+        throw SkipTestException("Test is disabled for OpenVINO 2018R5");
+#endif
     testLayerUsingCaffeModels("layer_eltwise");
 }
 

diff --git a/modules/dnn/test/test_onnx_importer.cpp b/modules/dnn/test/test_onnx_importer.cpp
@@ -164,6 +164,8 @@ TEST_P(Test_ONNX_layers, MultyInputs)
 
 TEST_P(Test_ONNX_layers, DynamicReshape)
 {
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        throw SkipTestException("");
     testONNXModels("dynamic_reshape");
 }
 
@@ -249,6 +251,10 @@ TEST_P(Test_ONNX_nets, VGG16)
     else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL) {
         lInf = 1.2e-4;
     }
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
+        l1 = 0.131;
+#endif
     testONNXModels("vgg16", pb, l1, lInf);
 }
 
@@ -327,7 +333,7 @@ TEST_P(Test_ONNX_nets, CNN_MNIST)
 TEST_P(Test_ONNX_nets, MobileNet_v2)
 {
     // output range: [-166; 317]
-    const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.38 : 7e-5;
+    const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.4 : 7e-5;
     const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 2.87 : 5e-4;
     testONNXModels("mobilenetv2", pb, l1, lInf);
 }
@@ -350,7 +356,17 @@ TEST_P(Test_ONNX_nets, LResNet100E_IR)
 
 TEST_P(Test_ONNX_nets, Emotion_ferplus)
 {
-    testONNXModels("emotion_ferplus", pb);
+    double l1 = default_l1;
+    double lInf = default_lInf;
+    // Output values are in range [-2.01109, 2.11111]
+    if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
+        l1 = 0.007;
+    else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
+    {
+        l1 = 0.021;
+        lInf = 0.034;
+    }
+    testONNXModels("emotion_ferplus", pb, l1, lInf);
 }
 
 TEST_P(Test_ONNX_nets, Inception_v2)
@@ -371,6 +387,10 @@ TEST_P(Test_ONNX_nets, DenseNet121)
 
 TEST_P(Test_ONNX_nets, Inception_v1)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("");
+#endif
     testONNXModels("inception_v1", pb);
 }
 

diff --git a/modules/dnn/test/test_tf_importer.cpp b/modules/dnn/test/test_tf_importer.cpp
@@ -241,6 +241,10 @@ TEST_P(Test_TensorFlow_layers, unfused_flatten)
 
 TEST_P(Test_TensorFlow_layers, leaky_relu)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
+        throw SkipTestException("");
+#endif
     runTensorFlowNet("leaky_relu_order1");
     runTensorFlowNet("leaky_relu_order2");
     runTensorFlowNet("leaky_relu_order3");
@@ -383,6 +387,10 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
 
 TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        throw SkipTestException("Unstable test case");
+#endif
     checkBackend();
     std::string proto = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pbtxt", false);
     std::string model = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pb", false);
@@ -560,6 +568,10 @@ TEST_P(Test_TensorFlow_layers, slice)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
         (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
         throw SkipTestException("");
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("");
+#endif
     runTensorFlowNet("slice_4d");
 }